Overview of deep learning-based cell image analysis. A typical analysis pipeline consists of a retraining module and an inference module: the inference module directly produces estimated metrics.

Recent advancements in deep learning have transformed the analysis of blood cell images and the classification of leukemia. By employing complex neural network architectures, such as convolutional ...

A computational method called scSurv, developed by researchers at Institute of Science Tokyo, links individual cells to ...

Using Early Biomarker Change and Treatment Adherence to Predict Risk of Relapse Among Patients With Chronic Myeloid Leukemia Who Are in Remission The imaging cohort consisted of positron emission ...

Completed phase 1a dose escalation study of the first oral ENPP1 inhibitor RBS2418 immunotherapy in subjects with metastatic solid tumors. SECN-15: A novel treatment option for patients with ...

In organelle imaging, segmentation aims to accurately delineate pixels or voxels corresponding to target organelles from background, noise, and other cellular structures in microscopy images, thereby ...

In the rapidly evolving field of drug discovery, single-cell analysis has become an invaluable tool for understanding cellular heterogeneity and molecular pathways. However, traditional single-cell ...

During early development, tissues and organs begin to form through the shifting, splitting, and growing of many thousands of cells. A team of researchers headed by MIT engineers has now developed a ...

Stanford Medicine researchers have built CRISPR-GPT, a large language model designed to automate the full arc of gene-editing ...

Researchers create a massive single-cell atlas of the aging mouse brain, revealing how epigenetic changes and "jumping genes" drive neurodegeneration.